Your Brain on Google: Patterns of Cerebral Activation ...

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Your Brain on Google: Patterns of Cerebral Activation during Internet Searching

Gary W. Small, M.D., Teena D. Moody, Ph.D., Prabha Siddarth, Ph.D., Susan Y. Bookheimer, Ph.D.

Objective: Previous research suggests that engaging in mentally stimulating tasks may improve brain health and cognitive abilities. Using computer search engines to find information on the Internet has become a frequent daily activity of people at any age, including middleaged and older adults. As a preliminary means of exploring the possible influence of Internet experience on brain activation patterns, the authors performed functional magnetic resonance imaging (MRI) of the brain in older persons during search engine use and explored whether prior search engine experience was associated with the pattern of brain activation during Internet use. Design: Cross-sectional, exploratory observational study Participants: The authors studied 24 subjects (age, 55?76 years) who were neurologically normal, of whom 12 had minimal Internet search engine experience (Net Naive group) and 12 had more extensive experience (Net Savvy group). The mean age and level of education were similar in the two groups. Measurements: Patterns of brain activation during functional MRI scanning were determined while subjects performed a novel Internet search task, or a control task of reading text on a computer screen formatted to simulate the prototypic layout of a printed book, where the content was matched in all respects, in comparison with a nontext control task. Results: The text reading task activated brain regions controlling language, reading, memory, and visual abilities, including left inferior frontal, temporal, posterior cingulate, parietal, and occipital regions, and both the magnitude and the extent of brain activation were similar in the Net Naive and Net Savvy groups. During the Internet search task, the Net Naive group showed an activation pattern similar to that of their text reading task, whereas the Net Savvy group demonstrated significant increases in signal intensity in additional regions controlling decision making, complex reasoning, and vision, including the frontal pole, anterior temporal region, anterior and posterior cingulate, and hippocampus. Internet searching was associated with a more than twofold increase in the extent of activation in the major regional clusters in the Net Savvy group compared with the Net Naive group (21,782 versus 8,646 total activated voxels). Conclusion: Although the present findings must be interpreted cautiously in light of the exploratory design of this study, they suggest that Internet searching may engage a greater extent of neural circuitry not activated while reading text pages but only in people with prior computer and Internet search experience. These observations suggest that in middle-aged and older adults, prior experience with Internet searching may alter the brain's responsiveness in neural circuits controlling decision making and complex reasoning.(Am J Geriatr Psychiatry 2009; 17:116 ?126)

Key Words: Brain activation, functional MRI, Internet search, middle-age and older adults, computer experience

Received September 22, 2008; revised November 4, 2008; accepted November 5, 2008. From the Department of Psychiatry and Biobehavioral Sciences and Semel Institute for Neuroscience and Human Behavior, (GWS, TDM, PS, SYB), the Mary S. Easton Center for Alzheimer's Disease Research and Center on Aging (GWS), University of California, Los Angeles, Los Angeles, CA. Send correspondence and reprint requests to Gary W. Small, M.D., Semel Institute, Suite 88 ?201, 760 Westwood Plaza, Los Angeles, CA 90024. e-mail: gsmall@mednet.ucla.edu.

? 2009 American Association for Geriatric Psychiatry

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R ecent research suggests that spending time in mentally challenging tasks may improve brain health and delay cognitive decline.1,2 With the rapid growth of computer technology and the use of the Internet in recent years, many people are engaging in the mental challenge of going online one or more times each day. Although younger individuals are more likely to use the Internet-- 88% of people aged 18 ?29 years currently go online--a large proportion of middle-age and older adults use the Internet regularly.3 The Pew Internet & American Life Project reported on Internet use patterns between January 9 and February 4, 2006, in a sample of 2,000 adults and found that 72% of people aged 51?59 years and 54% of those between age 60 and 69 years went online.4 On a typical day, nearly 50% of online activities involve searching the Internet for information; 40% of people aged 50 ? 64 years and 27% of those aged 65 and older are performing such searches.5

As the brain ages, a variety of structural and functional changes occur, including increased atrophy, regional reductions in glucose metabolism, and deposition of amyloid plaques and tau tangles.6 These structural and functional alterations are associated with declines in processing speed, inhibitory control, and working memory, among other cognitive abilities.7 Routine computer and Internet use may have an impact-- both positive and negative-- on the aging brain and these cognitive functions.8 ?10 The brain effects of computer activities, particularly video gaming, have been explored in young adults and children.11?13 Although the repeated mental activity of searching for information on the Internet could alter brain activation patterns in older adults, previous studies have not assessed brain function during search engine use and whether the degree of prior search engine use influences the extent and level of activations.

Activation imaging, which compares brain activity while subjects perform a task relative to a control or resting state, may reveal subtle alterations in brain function that may not be reflected in cognitive changes as measured by standardized neuropsychological testing.14 Functional magnetic resonance imaging (MRI) provides measures of signal intensity associated with relative cerebral blood flow during memory or other cognitive tasks.15 Activation imaging techniques offer a useful strat-

egy for studying brain effects of mental stimulation, and recent studies support the possible role of such mental stimulation in preserving brain function.1,2 Moreover, the Internet is an attractive technology for potentially enhancing brain function. Despite rapid growth in various computer-based tools claiming to enhance cognitive ability and brain function, there is a relative dearth of research supporting the effects of enhanced cognitive activity on brain function.16?18

In the present study, we focused on measuring brain activation patterns related to cognitive tasks involved in searching online. Because practice of mental tasks has been found to alter neural activation patterns,19 we hypothesized that prior experience of using online search engines would influence activation patterns. In particular, we examined the difference in brain activity in a task that emulated the normal reading processes to gain knowledge about specific subjects, in comparison with an Internet search task, wherein the user actively seeks out and chooses the most relevant information. We hypothesized that actively searching for information would preferentially engage neural circuits involved in integrating semantic information, working memory, and decision making, specifically in dorsal and ventral prefrontal cortex. To address these issues, we performed functional MRI of the brain in older persons during search engine use and determined whether prior search engine experience influenced the degree and extent of activations.

METHODS

Study Subjects

We studied 24 neurologically normal subjects with technically adequate MRI scans of the brain. These subjects were selected initially from a pool of 76 potential subjects recruited through advertisements. From this pool of subjects aged 55?78 years, we excluded left-handed volunteers and anyone who had dementia, other medical, psychiatric or neurologic conditions, including cerebrovascular disease or uncontrolled hypertension, or took drugs that could influence cognition. All subjects completed a questionnaire for rating their

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frequency of computer and Internet use (1?5 scale) and their self-assessment of Internet expertise (1? 4 scale). Based on the results of the questionnaire, subjects were assigned to one of two groups: the Net Naive group (minimal prior search engine experience) or the Net Savvy group (more extensive prior experience with computers and the Internet), until at least 14 subjects were enrolled in each group. A total of 28 subjects received magnetic resonance (MR) scans, and four of them with technically inadequate scans were excluded. One subject was excluded due to excessive head motion, two subjects were excluded due to technical errors caused by the scanner or goggles, and one subject was excluded due to a brain abnormality.

All studies were performed at the Semel Institute for Neuroscience & Human Behavior, and the Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles. Written informed consent was obtained from all the subjects in accordance with the University of California, Los Angeles Human Subjects Protection Committee procedures.

MRI Data Acquisition

Images were acquired using a Siemens Allegra 3T whole brain MRI scanner at the Ahmanson-Lovelace Brain Mapping Center at University of California, Los Angeles. We collected blood oxygenation leveldependent functional echo-planar images using a pulse sequence with the following parameters: repetition time (TR), 2.5 seconds; echo time, 35 msec; flip angle, 90?; 28 slices; voxel dimensions, 3.1 3.1 3.0 mm; field of view, 200 mm; and matrix, 64 64. Slices were acquired with interleaved order. The data collected during the first two TRs were discarded to allow for T1 equilibration. A matched-bandwidth high-resolution anatomic scan coplanar to the echoplanar images was acquired for each subject with TR, 5 seconds; echo time, 33 msec; flip angle, 90?; 28 slices; voxel dimensions, 1.6 1.6 3.0 mm, field of view, 200 mm; and matrix, 128 128.

Cognitive Tasks During Scanning

During functional MR scanning, subjects performed either a novel Internet search task or a book reading task. Both MR scan runs included blocks of a control task of viewing nontext bar images. During

each Internet task, subjects were instructed to obtain information on a specific topic, such as benefits of eating chocolate, mountains in the United States, planning a trip to the Galapagos, how to choose a car, walking for exercise, benefits of drinking coffee, or other topic areas of potential interest. To motivate subjects during the scanning session, they were told that they would be assessed for their knowledge of the topic after the scanning session. Subject groups did not differ significantly in the results of the knowledge-based assessments following the scanning sessions, indicating their equivalence in processing of task content (Table 1). Subjects pressed one of three response buttons to control the cursor for the simulated online search conditions within the MR scanner. For each stimulus block, subjects were first

TABLE 1. Characteristics of Subjectsa

Age (yr) Education (yr) Female, number (%) Frequency of computer

useb Frequency of Internet

useb Self-rating of Internet

expertisec Questionnaire scoresd

Internet Task Reading Task Ethnicity African American Asian White

Net Na?ve (N 12) 65.8 4.7 17.2 2.4

11 (92)

1.8 0.8

1.2 0.4

1.2 0.1

79.2 27.9 75.0 30.1

0 2 10

Net Savvy (N 12) 62.4 7.3 17.2 2.4

9 (75)

4.5 1.2

4.5 1.2

3.6 0.5

72.9 29.1 83.3 19.5

1 0 11

aValues are means standard deviations; significant group dif-

ferences tested using two-sample t tests. No significant differences were found according to age (two-sample t (22) 1.33, p 0.2), education (two-sample t (22) 0.71, p 0.4); or sex (twosample t (22) 1.08, p 0.2) for Savvys compared with Naives.

Significant group differences were found for frequency of computer use (two-sample t (22) 6.60, p 0.0001), frequency of Internet use (two-sample t(22) 9.38, p 0.0001), and self-rating of Internet expertise (two-sample t(22) 17.6 , p 0.0001).

bHigher values on 1?5 scale indicate greater frequency: 1 never or once a month; 2 once or twice a week; 3 four or five times a week; 4 once a day; and 5 several times a day.

cHigher values on 1? 4 scale indicate greater experience: 1 none; 2 minimal; 3 moderate; and 4 expert.

dValues are means standard deviations. No significant differ-

ences were found between groups for questionnaire scores for either

the Internet or reading versions of the task. Internet task, Savvys compared with Naives: two-sample t (22) 0.54, p 0.60. Reading task, Savvys compared with Naives: two-sample t (22) 0.80, p 0.43.

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given a topic they had to research, after which the search results would appear on the screen. In all trials, exactly three websites relevant to the topic appeared similarly to how they would on a search engine. Using the response buttons, subjects chose from three options presented on simulated web pages viewed through goggles (Fig. 1). This allowed the subject to press a button and "click" to advance to the next simulated web page, similar to a typical online search experience.

The text reading condition (Fig. 1) was designed to control for hand movement from operating the finger pad, visual and language stimulation from reading text and viewing photographs on web pages, and finger movement (subjects advanced the text page

using the finger pad buttons). Subjects were also told that they would be assessed on the information they learned from reading the simulated text pages. Thus, the stimuli were matched for content across the Internet and reading conditions. There were two differences between tasks; first, in the Internet task, subjects chose which of the three websites they wanted to visit first, whereas in the reading condition the subjects were instructed to press a specific button to advance to a text page, and subjects pressed the corresponding button to reveal the text. Second, in the reading task, the text revealed after pressing the button and was laid out in a typical book format but with a picture on the "page," whereas in the Internet task, the identical text and

FIGURE 1. Examples of Task Pages Presented During Functional MRI Scanning: Nontext Bar Images (Upper Left); Text Page (Upper Right); Internet Page With Search Options (Lower Left); and Internet Information Page (Lower Right)

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pictures were laid out like a typical website. Despite the differences in layout, the actual text and pictures were identical across conditions. The Internet layout included additional graphics, typical of actual Internet sites; however, pop-ups were not included (Fig. 1). Further, the stimuli were counterbalanced across subjects so that each topic appeared equally in the Internet and book formats. A nontext bar pressing task controlled for attention, decision making, and finger movement--subjects were asked to press the button corresponding to the highlighted bar images at regular intervals. Subjects performed separate runs of Internet and reading tasks. Each run was 4 minutes long, alternating six activation blocks with five 10-second blocks of the bar pressing control task. Each subject performed one run of the Internet task and one run of the reading task. Subjects were allowed 15 seconds for choosing a link and 27.5 seconds for reading the content of the web page. Similarly, subjects were allowed 15 seconds for reading the table of contents and 27.5 seconds for reading the book page. The order of task presentation was counterbalanced across subjects. Subjects were given instructions on the task before scanning and performed a short practice version of the task to confirm that they understood the task and could press buttons as instructed. The analysis combined the bulleted text and nonbulleted text for the Internet task, and combined the table of contents and the text page for the reading task. The order of tasks (Internet or book) was randomized across subjects.

Image Processing and Analysis

Image preprocessing and analysis were carried out using the Oxford, England, Centre for Functional MRI of the Brain (FMRIB)'s Software Library.20 Spatial smoothing was applied using a full-width halfmaximum Gaussian kernel of 5 mm. Preprocessing and analysis were run using fMRI Expert Analysis Tool version 5.91. To remove low-frequency artifacts, each functional run was temporally filtered using a high-pass cutoff of 100 seconds. For each functional run, motion correction was applied using 3-Da coregistration of each image to the middle image of the time series with Motion Correction using FMRIBs Linear Image Registration Tool.21

Subjects with head motion of more than 1.5 mm (one-half voxel) were not entered into further analy-

sis. The remaining head motion profiles were further examined for evidence of remaining motion artifact post head motion correction. For these scans, an independent components analysis was carried out using the FMRIBs Multivariate Exploratory Linear Optimized Decomposition into Independent Components tool.22 The spatial and temporal characteristics of each isolated component was examined, and components that were clearly related to motion or other sources of low- or high-frequency noise were removed. All statistical analyses were carried out both before and after denoising. The group statistical results, including the denoised dataset, did not differ qualitatively from those before denoising with Multivariate Exploratory Linear Optimized Decomposition into Independent Components.

Registration of the functional data followed a twostage process using linear registration with FMRIBs Linear Registration Tool: each functional run was first registered to a higher resolution T2-weighted matchedbandwidth anatomic image of each subject (7 df affine transforms), and then to the Montreal, Quebec, Canada, Neurological Institute (MNI) 152 standard template anatomic image (12 df affine transforms).

Blood oxygenation level-dependent signal during the experimental versus control tasks was convolved with a canonical double-gamma hemodynamic response function, which models the rise and the following undershoot along with its temporal derivative. Statistical analysis was first performed on each subject's individual functional run using general linear modeling by FMRIBs Improved Linear Model. The second step analysis combined the task versus control comparisons for each group separately (Savvy and Naive subjects), using a random effects model by FMRIBs Local Analysis of Mixed Effects.23,24 Resulting Z-statistic images were thresholded using cluster size determined by Z 2.3 and a (corrected) cluster significance threshold of p 0.05.25 We then directly contrasted the Internet versus reading tasks using the experimental task versus bar pressing control results as a mask. This approach limits the number of multiple comparisons conducted to those demonstrated to be significant for the activation tasks, reducing the risk of false-positive errors. We limited this comparison to the single, one-tailed contrast of Internet reading, because in the task

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